Method and System for Enhanced Content Recommendation

ABSTRACT

Method, system, and programs for providing content recommendation are disclosed. A first set of candidate content items may be generated based on a user profile, and a second set of candidate items may be generated based on the likelihood that the user will click a corresponding candidate content item in the second set. The candidate content items in the first and second sets may be ranked together using a learning model and presented to the user as content recommendations based on their rankings. The likelihood that the user will click a given candidate content item in the second set may be estimated based on similarities between the given content item and content items related to the given content item. Such a similarity may be computed based on activities performed by users who have viewed both the given content item and a related content item.

BACKGROUND

1. Technical Field

The present teaching relates to providing content. Specifically, thepresent teaching relates to methods and systems for providing onlinecontent.

2. Discussion of Technical Background

The Internet has made it possible for a user to electronically accessvirtually any content at any time and from any location. With theexplosion of information, it has become more and more important toprovide users with information that is relevant to the user and not justinformation in general. Further, as the Internet has become an importantsource of information for millions of users, including entertainment,and/or social connections, e.g., news, social interaction, movies,music, etc., it is critical to provide users with information they findvaluable.

Efforts have been made to allow users to readily access relevant and onthe point content. For example, topical portals have been developed thatare more subject matter oriented as compared to generic contentgathering systems such as traditional search engines. Example topicalportals include portals on finance, sports, news, weather, shopping,music, art, film, etc. Such topical portals allow users to accessinformation related to subject matters that these portals are directedto. Users have to go to different portals to access content of certainsubject matter, which is not convenient and not user centric.

Another line of efforts to enable users to easily access relevantcontent is via personalization, which aims at understanding each user'sindividual likings/interests/preferences so that an individualized userprofile for each user can be set up and can be used to select contentthat matches a user's interests. The underlying goal is to meet theminds of users in terms of content consumption. User profilestraditionally are constructed based on users' declared interests and/orinferred from, e.g., users' demographics. There have also been systemsthat identify users' interests based on observations made on users'interactions with content. A typical example of such user interactionwith content is click through rate (CTR).

CTR may have been the most commonly used measure to estimate users'interests. However, CTR is not the only type of information adequate tocapture information reflecting users' interests particularly given thatother different types of activities that a user may perform may alsoindicate or implicate user's interests. In addition, user reactions tocontent usually represent users' short term interests. Such observedshort term interests, when acquired in piece meal, as traditionalapproaches often do, can only lead to reactive, rather than proactive,services to users. Although short term interests are important, they maynot be sufficient to reach an understanding of the more persistent longterm interests of a user, which are crucial in terms of user retention.Most user interactions with content represent short term interests ofthe user so that relying on such short term interest behavior makes itdifficult to expand the understanding of the increasing range ofinterests of the user. When this is in combination with the fact thatsuch collected data is always the past behavior and collected passively,it creates a personalization bubble, making it difficult, if notimpossible, to discover other interests of a user unless the userinitiates some action to reveal new interests.

Yet another line of effort to allow users to access relevant content isto pooling content that may be of interest to users in accordance withtheir interests. Given the explosion of information on the Internet, itis not likely, even if possible, to evaluate all content accessible viathe Internet whenever there is a need to select content relevant to aparticular user. Thus, realistically, it is needed to identify a subsetor a pool of the Internet content for individual users or a subgroup ofusers who share interests based on some criteria so that content can beselected from this pool and recommended to users based on theirinterests for consumption.

Conventional approaches to creating such a subset of content areapplication centric. Each application carves out its own subset ofcontent in a manner that is specific to the application. For example,Amazon.com may have a content database related to products andinformation associated thereof created/updated based on informationrelated to its own users and/or interests of such users exhibited whenthey interact with Amazon.com. Based on knowledge about individualusers, Amazon.com may generate a pool for each user based on theirpurchasing preferences. Facebook may also have its own subset ofcontent, generated in a manner not only specific to Facebook but alsobased on user interests exhibited while they are active on Facebook. Asa user may be active in different applications (e.g., Amazon.com andFacebook) and with each application, they likely exhibit only part oftheir overall interests in connection with the nature of theapplication. Given that, each application can usually gainunderstanding, at best, of partial interests of users, making itdifficult to develop a subset of content that can be used to serve abroader range of users' interests.

Yet another line of effort is directed to personalized contentrecommendation, i.e., selecting content from a content database based onthe user's personalized profiles and recommending such identifiedcontent to the user. Conventional solutions focus on relevance, i.e.,the relevance between the content and the user's past interests. Forexample, a user's profile indicating a set of features (e.g., terms,phrases, topics, categories) of content viewed by the user in the pastis typically extracted for comparison with the features in the contents.However, such relevance based content recommendation techniques arelimited in that they require feature extraction from both the userprofile and contents to be accurate so that features in the contents maybe matched to the features in the user profile for recommendation.

Another line of effort is directed to recommending contents based onuser activity information. For example, some conventional systemsanalyze user activities and generate a bipartite graph indicatingwhether users viewed certain content items. For instance, an edge may beestablished in the bipartite graph between user A and content item A toindicate user A viewed content item A, another edge may be establishedin the bipartite graph between user A and content item C to indicateuser A viewed content item C, a third edge may be established in thebipartite graph between user B and content item C to indicate user Bviewed content item C, and so on. Based on this bipartite graph, thesesystems may recommend content item C to a given user who viewed contentitem A because user A, who is deemed to be similar to the given user bythose systems, viewed content items A and C. To these systems, user A'sviewing of content items A and C correlates the given user's interestand thus content item C should be recommended to the user after thegiven user viewed content item A. However, such an approach is limitedbecause the bipartite graph may not accurately capture user A'sinterest. For example, lacking of an edge between user A and a contentitem, say content item D, does not necessarily mean user A is notinterested in content item D. There could be a number of other reasonswhy user A did not view content item D—for example, content item D maynot have been even presented to user A or user A may not have discoveredcontent item D. As another example, the edge between user A and contentitem C indicating user A viewed content item C may also not necessarilymean user A is interested in content item C. For instance, user A mayhave viewed content item C merely for a quick overview and decidescontent item C is not of interest to him/her-self. On the other hand,other user activities such as user click activities during viewing ofthe content items may provide further insights into user interests incontent items. They may be used to enhance and enrich the conventionaluser-activity based content recommendation approach.

Accordingly, there is at least a need to enhance conventional contentrecommendation techniques.

SUMMARY

The present teaching relates to providing content. Specifically, thepresent teaching relates to methods and systems for providing onlinecontent.

In one example, a method, implemented on a machine having at least oneprocessor, storage, and a communication platform connected to a network,for presenting providing content recommendations.

In accordance with the present teaching, for recommending content itemsto a user, a first set of candidate content items may be generated basedon a user profile, and a second set of candidate items may be generatedbased on the likelihood that the user will click a correspondingcandidate content item in the second set. The candidate content items inthe first and second sets may be ranked together using a learning modeland presented to the user as content recommendations based on theirrankings.

For generating the first set of candidate content items forrecommendation to the user, information in the user's profile indicatingcontent features that have been viewed by the user may be obtained. Thenumber of times the user has viewed a particular one of these contentfeatures may be compared with average number of times other users haveviewed that content feature. Such a comparison may be carried out foreach content feature that has been viewed by the user. The contentfeatures that have been viewed by the user may then be ranked based onthe results of such comparisons. A number of content features that havebeen viewed by the user may be selected based on their rankings toreflect the user's interest in content features. The first set of thecandidate content items may be generated from a content storage based onthe number of selected content features.

For generating the second set of candidate content items, the likelihoodthat the user will click a given candidate content item may be estimatedbased on similarities between the given content item and content itemsrelated to the given content item and viewed by the user previously. Asimilarity between the given content item and a related content item maybe generated based on activities performed by users who have viewed boththe given content item and the related content item. The user activitiesmay include clicking, typing, scrolling, dwelling, forwarding,commenting, and/or any other types(s) of activities by those usersduring viewing of the given content item and the related content item.In implementations, for computing such a similarity, a user activityvector for the given candidate content item may be generated. The valuesof the generated user activity vector may indicate weighted useractivities performed by corresponding users during viewing of the givencandidate content item. A user activity vector for the related contentitem may be similarly generated. The similarity between the givencandidate content item and the related content item may be estimated bycomparing the two user activity vectors. Similarities between the givencandidate content item and each related content item may be estimated inthis fashion and aggregated. Based on the aggregated similaritiesbetween the given candidate content item and the related content itemsand whether the user has clicked the related content items, thelikelihood that the user will click the given candidate content item maybe determined. If the likelihood is high enough, the given candidatecontent item may be included in the second set of candidate contentitems.

The candidate content items in the first and second sets may be rankedtogether using a learning model. In some implementations, the learningmodel may be trained using user information, content information,user-content cross information, and/or any other type(s) of information.Candidate content items in the first and second sets may be presented tothe user as content recommendations based on their rankings determinedusing the learning model.

Other concepts relate to software for implementing the enhanced contentrecommendations. A software product, in accord with this concept,includes at least one machine-readable non-transitory medium andinformation carried by the medium. The information carried by the mediummay be executable program code data regarding parameters in associationwith a request or operational parameters, such as information related toa user, a request, or a social group, etc.

In one example, a machine readable and non-transitory medium havinginformation recorded thereon for recommending content items, where whenthe information is read by the machine, causes the machine to obtain auser profile characterizing interests of the user; generate a first setof candidate content items based on the user profile; generate a secondset of candidate content items based on a likelihood that the userclicks a corresponding candidate content item in the second set, whereineach likelihood is estimated based on similarities between the candidatecontent items in the second set and one or more content items that werepreviously viewed by the user; rank each of the candidate content itemsin the first set and the second set; and provide, based on the rankings,the candidate content items in the first and second sets as contentrecommendations to the user.

Additional advantages and novel features will be set forth in part inthe description which follows, and in part will become apparent to thoseskilled in the art upon examination of the following and theaccompanying drawings or may be learned by production or operation ofthe examples. The advantages of the present teachings may be realizedand attained by practice or use of various aspects of the methodologies,instrumentalities and combinations set forth in the detailed examplesdiscussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The methods, systems and/or programming described herein are furtherdescribed in terms of exemplary embodiments. These exemplary embodimentsare described in detail with reference to the drawings. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 depicts an exemplary system for personalized contentrecommendation, according to an embodiment of the present teaching;

FIG. 2 is a flowchart of an exemplary process for providing contentrecommendation, according to various embodiments of the presentteaching;

FIG. 3 illustrates one example of the user-profile based candidate itemselection unit shown in FIG. 1, according to an embodiment of thepresent teaching;

FIG. 4 illustrates an exemplary method that may be implemented by therelevance-based content similarity determination module shown in FIG. 3,according to various embodiments of the present teaching;

FIG. 5 illustrates one example of a user-activity based candidate itemselection unit shown in FIG. 1, according to an embodiment of thepresent teaching;

FIG. 6 conceptually illustrates one example of user activity informationassociated with a set content items;

FIG. 7 conceptually illustrates how a similarity between two contentitems can be determined based on their user activity information,according to an embodiment of the present teaching;

FIG. 8 illustrates another example how a similarity between two contentitems can be determined based on their user activity information,according to an embodiment of the present teaching;

FIG. 9 conceptually illustrates how a similarity between content itemsmay be determined based on user activity vectors associated with thecontent items, according to an embodiment of the present teaching;

FIG. 10 is a flowchart of an exemplary process for determining asimilarity between two content items, according to one embodiment of thepresent teaching.

FIG. 11 is a flowchart of another exemplary process for determining asimilarity between two content items, according to one embodiment of thepresent teaching;

FIG. 12 is a flowchart of an exemplary process for estimating likelihoodthat a user will click a given content item, according to an embodimentof the present teaching;

FIG. 13 is an exemplary system diagram of a machine learning engineshown in FIG. 1, according to an embodiment of the present teaching;

FIG. 14 illustrates examples of user side features that may be used totrain a learning model;

FIG. 15 illustrates examples of content side features that may be usedto train a learning model;

FIG. 16 illustrates an exemplary system diagram of the unified rankingunit shown in FIG. 1, according to one embodiment of the presentteaching.

FIG. 17 depicts the architecture of a mobile device which can be used toimplement a specialized system incorporating the present teaching; and

FIG. 18 depicts the architecture of a computer which can be used toimplement a specialized system incorporating the present teaching.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent to those skilledin the art that the present teachings may be practiced without suchdetails. In other instances, well known methods, procedures, components,and/or circuitry have been described at a relatively high-level, withoutdetail, in order to avoid unnecessarily obscuring aspects of the presentteachings.

The present teaching relates to recommending on-line content to a user.Particularly, the present teaching relates to a system, method, and/orprograms for personalized content recommendation that addresses theshortcomings associated the conventional content recommendationsolutions in personalization, content database, and recommendingpersonalized content. The present teaching may be realized as aspecialized and networked system by utilizing one or more computingdevices (e.g., mobile phone, personal computer, etc.) and networkcommunications (wired or wireless).

In accordance with the present teaching, a first set of candidatecontent items may be generated based on a user profile. The user profilemay comprise information indicating content features that have beenviewed by the user. The individual content features in the user profilemay be ranked by comparing a number of times the user viewed acorresponding content feature and an average number of times thiscontent feature was viewed by a general population of users. A number ofcontent features may be selected based on the rankings of the contentfeatures. Based on the selected content features, the first set ofcandidate content items may be generated for recommendation to the user.

In accordance with the present teaching, a second set of candidate itemsmay be generated based on the likelihood that the user will click acorresponding candidate content item in the second set. In someimplementations, the likelihood that the user will click a givencandidate content item may be estimated based on similarities betweenthe given candidate content item and related content items that wereviewed by the user previously. A similarity between the given candidatecontent item and a related content item may be generated based onactivities by users who have viewed both the given candidate contentitem and the related content item. The activities may include clicking,typing, scrolling, dwelling, forwarding, commenting, and/or any othertypes(s) of activities by those users during viewing of the givencandidate content item and the given related content item. Inimplementations, for computing such a similarity, a user activity vectorfor the given candidate content item may be generated having variousweights associated with corresponding activities that were performed bycorresponding users during viewing of the given candidate content item.A user activity vector for a given related content item may be similarlygenerated. The similarity between the given candidate content item and arelated content item may then be computed by comparing the two useractivity vectors. The similarities between the given candidate contentitem and related content items may be aggregated. The likelihood thatthe user will click the given candidate content item may be determinedbased on the aggregated similarities and whether the related contentitems were clicked by the user. If the likelihood is high enough, thegiven candidate content item may be included in the second set ofcandidate content items.

After the first and second sets of candidate content items aregenerated, in accordance with the present teaching, the candidatecontent items in the first and second sets may be ranked. In someexamples, a learning model may be used to rank the candidate contentitems in the first and second sets. The learning model may be trainedusing a set of training data including user features, content features,user-content cross features, and/or any other features. These featuresmay be gathered and fed into the learning model to train the learningmodel. The trained learning model may then be used to rank the candidatecontent items in the first and second sets for presentation to the user.

FIG. 1 depicts an exemplary system 10 for personalized contentrecommendation, according to an embodiment of the present teaching. Asshown in this example, system 10 may comprise a personalized contentrecommendation module 100, a machine learning engine 108, storage 112,and/or any other components. As shown, system 10 may be operativelyconnected to or include content source(s) 120, knowledge archives 130,third party platform(s) 140, and external resources 150. Contentsource(s) 120 may include any source of on-line content such as on-linenews, published papers, blogs, on-line tabloids, magazines, audiocontent, image content, and video content. Examples of contents providedby content source(s) 120 may include contents from content provider suchas Yahoo! Finance, Yahoo! Sports, CNN, and ESPN; may include textual,multi-media contents and/or any other form of content suitable for userinteraction or viewing via the world wide web; may include social mediacontents accessible on a social media site, such as Facebook, twitter,Reddit, and/or or any other social media site; may include licensedcontents from providers such as AP and Reuters; may include contentscrawled and indexed from various sources on the Internet; and/or mayinclude any other types of contents. Content sources 120 provide avariety of content to system 10 for providing personalized contentrecommendation. It is understood that content sources 120 may includesources of contents that are not necessarily accessible via Internet.For example, content sources 120 may include sources of contentsaccessible via a local communication network, such as an intranet.

Knowledge archives 130 may include any depository of knowledge relatedinformation, including an on-line encyclopedia such as Wikipedia orindexing system such as an on-line dictionary. Knowledge archives 130may be utilized for its content as well as its categorization orindexing systems. Knowledge archives 130 may provide classificationsystem to assist with the classification of both the preferences of useras well as classification of content. Knowledge archives 130, such asWikipedia may have hundreds of thousands to millions of classificationsand sub-classifications, which may be organized in hierarchies. Thoseclassifications may be used by system 10 to determine or selectpersonalized content to be recommended to a user. For example, they mayhelp system 10 to understand how one category of contents relates toanother category of contents. They may help system 10 to maneuverbetween higher levels on the hierarchy without having to move up anddown the subcategories. The categories or classification structure inKnowledge archives 130 may be used for constructing multidimensionalcontent vectors and multidimensional user profile vectors, which may beutilized by personalized content recommendation module 100 to matchpersonalized content.

The third party platform(s) 140 may include any platform provided by athird party service provider affiliated with, e.g., the system 10.Examples of third party platforms may include, but not limited to,social networking sites like Facebook, Twitter, LinkedIn, Google+, mailservers such as Gmail, online community such as America Online, and/orany other type(s) of third party platform(s). Third party platforms 140may provide system 10 contents as well as information that may beutilized to understand insights of user preferences and behaviors. Suchinsights may be used to help system 10 to recommend personalized contentto individual users. As illustration, without limitation, informationfrom a third party platform on user activities performed on the thirdparty platform may reveal users' interests exhibited on the third partyplatform, content that the users consumed on the third party platform,and/or any other user information from the third party platform that maybe used to enhance the personalization of content stream provided to aparticular user of system 10. For example, when information about alarge user population can be accessed from one or more third partyplatforms 140, system 10 can rely on data about a large population toestablish a baseline interest profile to estimate the interests ofindividual users more precise and reliable, e.g., by comparing interestdata with respect to a particular user with the baseline interestprofile which will capture the user's interests with a high level ofcertainty.

External resources 150 may provide various types of content to system10, including but not limited to, streaming contents, static contents,and sponsored contents. External resources 150 may include any partyassociated with an advertising entity that is associated with system 10.Such an external resource may provide content to be included in therecommendation to a user, including, but not limited to, advertisementsfrom an advertisement content database (not shown), information relatedto target audience for each advertisement, and/or information related toadvertisement taxonomy that may be used to identify advertisement inappropriate categories. Advertising contents may be presented either asa part of a content stream or a standalone advertisement such as apop-up window, and can be placed either according to a designatedad-space or strategically around or within the associated contentstream. In some implementations, appropriate advertising content may beselected by the personalized content recommendation module 100 in orderfor it to be inserted into a content stream. The personalization ofadvertising content may be based on a variety types of information suchas user profile 110 or user activities. In some implementations, contenttaxonomy, advertisement taxonomy, and user information, including userprofile or activities, may all be used to determine appropriateadvertisement given the content related to certain subject matter to bepresented to a specific user that has certain preferences.

In some implementations, system 10 may be configured to identify and/orprocess contents from content source(s) 120, knowledge archives 130,3^(rd) party platforms 140, and/or external resources 150 foridentifying personalized content recommendations to user. For instance,when new content is identified from content source 120 or third partyplatform(s) 140, system 10 may be configured to analyze the new contentto understand concepts or subject matter embodied in the new contents.Such obtained concepts can be mapped to one or more categories in acontent taxonomy (not shown) associated with system 10. The contenttaxonomy may include an organized structure of concepts or categories ofconcepts and it may contain a few hundred classifications of a fewthousand. In addition, the knowledge archives 130 may also providemillions of concepts, which may or may not be structured in a similarmanner as the content taxonomy. The content taxonomy and, in someexamples, the knowledge archives 130 may be used as a universal interestspace. Concepts estimated from the new content can be mapped to thisuniversal interest space and a high dimensional content feature vectormay be constructed for the new content and used to characterize the newcontent. Similarly, for a user, a personal interest profile may beconstructed, mapping the user's interests, characterized as concepts, tothe universal interest space so that a high dimensional user featurevector can be constructed with the user's interests levels populated inthe general vector.

The personalized content recommendation module 100 may be configured toprovide personalized content to a user. In some implementations,operation(s) of personalized content recommendation module 100 may betriggered when the user engages system 10 through a client device 160associated with user. Client device 160 may include a smart phone, atablet, a laptop computer, a desktop computer, a wearable device, anembedded computing device (such as in a car), a game console, a set-topbox, a printer, or any other type of client device. User informationincludes, e.g., user id, login information, session information, useractivity information (e.g., click, typing, browsing, and/or any othertype user activities), or search information (e.g., search terms enteredby the user to system 10). Some user information reflects user'sinterests. Information related to any other user may also be receivedfrom the client device 160 operated by such users so that interests of agroup of users may also be collected and utilized. User may interactwith system 10 via any suitable communications channel including a wiredlink and/or a wireless link. The user information received from userdevices 160 may be utilized by the personalized content recommendationmodule 100 to locate content that is customized with respect toindividual users for recommending to the users on their respectiveclient device 160. In various implementations, the personalized contentrecommended to the user may include personalized content streampresented in an application running on the client device 160.

In some implementations, as shown in this example, the personalizedcontent recommendation module 100 may include a unified ranking unit102, a user activity-based candidate content selection unit 104, auser-profile based candidate content selection unit 106, a machinelearning engine 108, and/or any other appropriate components. As shown,the user-profile based candidate content selection unit 106 may beconfigured to identify content appropriate for the user based on theprofile related to the user. In operation, the user-profile basedcandidate content selection unit 106 receives a user profile 110 of auser (user #1 in this example) and various content items from a contentdatabase 114 in the storage 112 as inputs. The user-profile basedcandidate content selection unit 106 may be configured to generate afirst set of candidate content items based on these inputs.

The user-activity based candidate content selection unit 104 may beconfigured to identify appropriate content for a user based onactivities of the user. In operation, the user-activity based candidatecontent selection unit 104 may receive information related to useractivities, e.g., from the user activity database 116 and select asecond set of candidate content items from the content database 114 thatare deemed as appropriate given the observed user activities. Theunified ranking unit 102 may be configured to receive the first andsecond candidate content items (from the user-profile based candidatecontent selection unit 106 and user-activity based candidate contentselection unit 104, respectively), and rank the content items receivedbased on a learning model provided by the machine learning engine 108.As will be described later, the machine learning engine 108 may beconfigured to train learning model(s), which may be used by the unifiedranking unit 102 to rank the candidate content items in the first andsecond sets content items to be recommended to a user.

Storage 112 may be configured to store various types of information thatmay be used to assist personalized content recommendation module 100 toprovide personalized content recommendations. As shown in this example,storage 112 may include, but is not limited to, a content database 114configured to store information related to content items, and a useractivity database 116 configured to store information related to useractivities. Content database 114 may include content items that can bepresented to the users and/or Meta information associated therewith. Forexample, content items may include, but not limited to, articles,movies, music clips, photos, animation, sounds, games, interactiveapplications (e.g., java applets), contents in an app on a portabledevice, virtual reality contents, and/or any other type of contentitems. In some implementations, the content database 114 may bestructured so that it may store personalized content pool for individualusers. In this case, content items in the content pool may be generatedand retained with respect to individual users. In implementations, thecontent pools in content database 114 may be organized as a tieredsystem with both the general content pool and personalized individualcontent pools for different users. In some implementation, in a contentpool for a user, the content items itself may not be physically presentbut is operational via links, pointers, or indices which providereferences to where the actual content is stored in the general contentpool.

Content database 114 may be dynamically updated by system 10. Contentitem in the content database may come and go and decisions may be madebased on the dynamic information of the users, the content itself, aswell as other types of information. For example, without limitation,when the performance of content deteriorates, e.g., low level ofinterests exhibited from users, system 10 may decide to purge it fromthe content database 114. When content becomes stale or outdated, it mayalso be removed from the content database 114. When there is a newlydetected interest from a user, system 10 may fetch new content aligningwith the newly discovered interests and then update the content database114 by, e.g., insert the content into a relevant content pool in thedatabase 114.

The user activity database 116 may be configured to store informationrelated to activities that user conducted. User events may be animportant source of observations as to content performance and userinterest dynamics. Information of user activities may be analyzed bysystem 10 in determining inferred user interests or dynamics of the userinterests. When new interests are identified for a user, contentappropriate for the new interests may be identified and used to updatecontent database 114. When fetching new content (e.g., from contentsources 120, knowledge archives 130, 3^(rd) party platform(s) 140,and/or external resources 150), system 10 may invoke a content crawler(not shown in this example) to gather new content, which may then beanalyzed and evaluated as to its quality and performance before system10 decides whether it will be included in the content pool associatedwith the user in the content database 114. Content may also be removedfrom content database 114 when it is no longer relevant, e.g., eitherbecause other users are not considering it to be of high quality orbecause it is no longer timely. As content is constantly changing,updating content database 114 may also be performed constantly ordynamically to change or update the content database 114 so thatpotentially high relevant, high quality, and timely content can be madeavailable to the personalized content recommendation module 100 forrecommendation.

The user activity database 116 may be configured to store differenttypes of user activity information in connection with the content itemsstored in the content database 114. Examples of user activityinformation that may be stored in the user activity database 116 mayinclude information indicating scroll activities, scroll time, overalldwell time, dwell time by sections in the given content item, linksclicked, position clicked, typing activities, and/or any other activityinformation indicating various aspects how users viewed and/orinteracted with a given content item. For example, the user activityinformation corresponding to a given content item may indicate useractivities engaged by one or more of users during viewing of the givencontent item. As an illustration, without limitation, the user activityinformation corresponding to the given content item may includeinformation indicating a first number of specific users that have viewedthe given content item, a second number of specific users that haveviewed and clicked the given content item, and a third number ofspecific users that have not viewed given content item. For those userswho have viewed the given content item, the user activity informationmay include information indicating scroll activities, scroll time,overall dwell time, dwell time by sections in the given content item,links clicked, position clicked, typing activities, and/or any otheractivity information indicating various aspects how the users viewedand/or interacted with the given content item.

With system 10 having been generally described above, various aspects ofpersonalized content recommendation module 100 will now be describedwith reference to FIGS. 2-12.

FIG. 2 is a flowchart of an exemplary process 200 for providing contentrecommendation, according to various embodiments of the presentteaching. As shown, at 202, presence of a user may be detected, and auser profile as well as session information associated with the user maybe obtained. The user profile obtained at 202 may include informationindicating content features that have been viewed in the past by theuser. For example, such content features in the user profile may includephrases, keywords, specific content identification (e.g., a title of thecontent item), content topics, content categories and/or any othercontent features that have been viewed by the user in the past. In someimplementations, these features may be indexed in accordance with thefrequencies at which the user has viewed them. As an illustration, agiven user profile obtained at 202 may indicate that the use has viewedcontents related to sports (e.g., a topic or category) most often in thepast, content items related to finance second most often in the past andso on. As another illustration, the given user profile obtained at 202may indicate the user has viewed content items containing keywords“LeBron James” most often in the past, and contents containing keywords“Cleveland city council” second most often, and so on. The user sessioninformation obtained at 202 may include information indicating one ormore content items that have been viewed by the user in a session orsessions engaged by the user (e.g., the current session and/or a numberof historical sessions).

At 204, a first set of candidate content items may be generated based onthe user profile obtained at 202. The first set of candidate contentitems may be generated based on content items stored in the contentdatabase. The first set of candidate content items at 204 may bedetermined based on considerations, e.g., whether the content itemrelates to the user's interests according to the user profile. Theinterests of a user as recorded in the user profile may be determinedbased on, e.g., declared interests and/or interests that are inferredfor the user based on, e.g., user activities observed.

At 208, a second set of candidate content items may be selected from thecontent database 114 based on information related to user activities.For example, based on activities of a user on various content itemspreviously presented to the user, the likelihood that the user will beinterested in a candidate content item may be estimated. The higher thelikelihood estimated, a higher possibility that the user will like tocontent item. In operation, to select a candidate content item in thesecond set, system 10 may perform the following: for each candidatecontent item in the content database, determining a set of relatedcontent items that are related to the candidate content item and thathave been previously presented and viewed by the user; determining asimilarity between the candidate content item and each related contentitem; weighting clicking or non-clicking event by the user duringviewing of each related content item with the correspondingsimilarities; computing the likelihood that the user will click thecandidate content item based on aggregation of the weighted clicking ornon-clicking events by the user divided by total number of viewing ofthe related content items by the user; and including the candidatecontent item in the second set of candidate content items based on thelikelihood of the candidate content item (e.g., if it exceeds apredetermined threshold).

At 210, the candidate content items in the first and second sets, asdetermined at 204 and 208 respectively, may be ranked. In someimplementations, operation(s) at 210 may involve ranking the candidatecontent items in the first and second sets using a learning model.

At 212, the candidate content items may be presented to the user ascontent recommendations based on their rankings determined at 210.

FIG. 3 illustrates an example of the user-profile based candidatecontent selection unit 106 shown in FIG. 1. It will be described withreference to FIGS. 1-2. As shown in this example, the user-profile basedcandidate content selection unit 106 may be configured to include a userprofile processor 302, a user-content feature vector builder 304, arelevance-based content similarity determination module 308, a candidatecontent selection module 310, and/or any other module. As shown, theuser profile processor 302 may be configured to receive a user profileassociated with a user. Such a user profile may include declaredinterests of the user or inferred interests of the user. The inferredinterests of the user may be represented using a feature vectorconstructed based on, e.g., keywords, phrases, topics, categories, orany other feature associated with content items viewed by the userpreviously. This feature vector may be constructed based on, e.g.,user's activities on the content items previously viewed and acted on.This feature vector may represent inferred interests of the usercaptured by the values in the vector against different attributes. Insome embodiments, the feature vector representing the inferred userinterests may be stored in the user profile. In other embodiments, sucha feature vector related to inferred user interests may be storedelsewhere and input to system 10 from a third party vendor (now shown).

The user profile processor 302 may be configured to forward the userprofile (and the feature vector representing user's inferred interests)to the user-content feature vector builder 304. The user-content featurevector builder 304 may then build a user-content feature vector for thegiven user. The user-content feature vector built by the user-contentfeature vector builder 304 may contain components, each of which mayindicate that a particular user feature has been viewed by the givenuser for a corresponding number of times. Such a user-content featurevector is then sent to the relevant-based content similaritydetermination module 308. As illustrated, the relevance-based contentsimilarity determination module 308 may be configured to determine thesimilarity between a content item and the user's interests. For example,when the relevance-based content similarity determination module 308receives the user-content feature vector from the user-content featurevector builder 304, it may also receive a feature vector 306 thatprovides the average interest of all users with respect to this contentitem. The average user feature list may be prepared and/or updated basedon, e.g., features collected from observations made on all users. Thisaverage user feature list may be offline or dynamically trained. Asillustrated, the average user feature list 306 may contain entriesindicating features contained in content items (keywords, phrases,topics, etc.) that have been viewed by different users in the pastwithin, e.g., a certain period of time. The average features may bedetermined based on, e.g., the total number of times being viewed by auser divided by the number of users who have viewed the content item.Based on the average user feature list 306 and the user-content featurevector, the relevance-based content similarity determination module 308may determine, for each feature, a relative score of the user ascompared to the average score from a group of users. The relative scorefor each feature may be a ratio or any other form that indicates howmuch the feature from the user deviates from the feature from the groupof users representing, e.g., the general population. When the featurefrom the user is significantly higher than that of the generalpopulation, it may be considered that the user is interested in contentitems that are associated with the feature. A set of features may beselected to guide how to proceed to identify content items that exhibitthe features that are ranked high in comparison with the generalpopulation.

Based on the results from the relevance-based content similaritydetermination module 308, the candidate selection module 310 selects,from the content database, content items that have the features that areranked high by the relevance-based content similarity determinationmodule 308. For example, the candidate selection module 310 may beconfigured to select content items that contain top features rankedbased on the relative scores determined by the relevance-based contentsimilarity determination module 308.

FIG. 4 illustrates an exemplary method 400 that may be implemented bythe relevance-based content similarity determination module 308 shown inFIG. 3. As shown, at 402 a user-content feature vector associated with auser may be received. The user-content feature vector received at 402corresponds to a vector for the user constructed based on observation onthe behavior of the user. The user-content feature vector may includevarious attributes or features or components, each of which correspondsto a feature that may occur in content. For example, a feature can be aword, a phrase, or any other features observed from content. Eachattribute may have a value which may indicate a number of times acorresponding content including the attribute or feature has been viewedby the user. At 404, an average user content feature list may bereceived. The average user content feature list received at 404corresponds to a user-content feature vector constructed based onobservations made on a group of users representing the generalpopulation of users. The average user-content feature list may compriseattributes, each of which indicates a number of times content includingthe corresponding feature has been viewed by the general population(represented by a group of users based on which the average user-contentfeature list is constructed).

At 406, for each feature in the user-content feature vector, thecorresponding number of times the user has viewed content including thefeature may be compared with the corresponding average number of times auser from the general population has viewed content with that feature.As an illustration, assuming a user-content feature vector) (f₁ . . .f_(i) . . . f_(n)) which has attribute or feature values (t₁ . . . t_(i). . . t_(n))_(u), respectively, indicating that the user has viewedcontent including feature f₁ in t₁ times, viewed content includingfeature f₂ in t₂ times, . . . etc. In some examples, the operation(s) at406 may involve normalizing (t₁ . . . t_(i) . . . t_(n))_(u) to (w₁ . .. w_(i) . . . w_(n))_(u). This may be done by dividing ti with thesummation of (t₁ . . . t_(i) . . . t_(n)). In an average user-contentfeature list, there are corresponding features and feature values,representing the average number of times the general population hasviewed content with each particular feature. The attribute values in theaverage user-content feature list may also be normalized. Then theoperation(s) at 406 may divide each w_(i) in the user-content featurevector by the average value for the corresponding feature, i.e.,avg(w_(i)) in the average user content-feature list to get a sense thestrength of the user interests in content including each particularfeature as compared with the interests in the same features from anaverage user.

At 408, top features may be determined for the user based on thecomparison at 406. The more the features value of the user deviates fromthat of an average user, more likely it is that the user has aparticular interest in content including that feature. At 410, a set ofcandidate content items may be generated based on the top features forthe user as determined at 408. For example, a set of top features may bedetermined by selecting those features which have features values thatare the most significantly higher than that of the corresponding featurevalues from the average user-content feature list. Such identified topfeatures may then be used to guide how content is selected from thecontent database that are deemed to most likely meet the interests ofthe user. Operation(s) at 410 may include querying the content from thecontent database using the selected top features as key or index toretrieve contents considered to be most relevant to the user interests.

FIG. 5 illustrates one example of a user-activity based candidatecontent selection unit 104 shown in FIG. 1. It will be described inconnection with FIG. 1. As shown in this example, the user-activitybased candidate content selection unit 104 may be configured to includea user session configuration module 502, a content item informationmodule 504, a user content activity information module 506, an activitybased content similarity determination module 510, a content item clickprobability determination module 508, a candidate selection module 512,and/or any other modules. As illustrated herein, the user sessionconfiguration module 502 may be configured to receive user sessioninformation regarding one or more user sessions associated with a givenuser. Based on the user session information received, the user sessionconfiguration module 502 may determine one or more content items thatwere viewed by the user during these user sessions. For example, theuser session information received may indicate that the user has viewed4 content items in a user session or that the user has reviewed 7content items in 3 user sessions. Accordingly, the user session contentdetermination module 502 may identify those content items viewed by theuser and forward these content items or identification informationthereof to the content item information module 504.

The content item information module 504 may be configured to retrievesuch content items and/or information thereof from the content database,such as the content database 114 shown in FIG. 1.

The user content activity information module 506 may be configured toretrieve or receive user activity information with respect to the givencontent item(s) and analyze the obtained user activity information. FIG.6 conceptually illustrates one example of user activity informationrecording activities in association with various content items observedin some users. As illustrated, the user activity information 600regarding activities of a set of users 604 a-i during viewing thecontent items 602 a-f may include information on different types of useractivities. As conceptually shown in FIG. 6, information regarding thefirst illustrated type of user activity may indicate whether users 602a-f viewed content items 602 a-f as illustrated in the first column(from the left) of FIG. 6. In FIG. 6, when a user did view a contentitem, there is an edge between the content item and the user (in thefirst column from the left of FIG. 6). For example, the edges emanatingfrom content item 602 a indicates that users 604 a, 604 b, and 604 dhave viewed the content item 602 a. Similarly, edges emanating from item602 b indicate that users 604 b, 604 d, and 604 f have viewed thecontent item 602 b.

Another illustrated type of user activity information is related toclick-through activity. In the second column of FIG. 6, it is shown thatfor each content item, which user have clicked-through the content item.That is, an edge between a content item and a user in the second columnof FIG. 6 indicates that the user clicked-through the content duringviewing of the content item. For example, the edge connecting contentitem 602 a and user 604 b indicates that the user 604 b clicked thecontent item 602 a during viewing of the content item 602 a. Similarly,the edge connecting content item 602 b and user 604 e and 604 gindicates that both user 604 e and 604 g have clicked the content item602 b during viewing of the content item 602 b.

The user activity information as illustrated in the first and secondcolumn from the left of FIG. 6 indicates whether a user viewing acontent item has clicked the content item. Such information can be fusedtogether to reveal integrated information. The fused information can berepresented by a fusion graph as illustrated in the third column of FIG.6. The fusion graph may be obtained by analyzing information containedin the first and second columns of FIG. 6. For example, if an edgeexists between a content item and a user in both the first and secondcolumns of FIG. 6, it means that the user not only viewed the contentitem but also clicked-through the content item, evidencing a strongerinterest of the user in the content item. In this case, there will be asolid edge between the user and the content item in the fused graph, asillustrated in the fusion graph in FIG. 6 between content item 602 a anduser 604 b. On the other hand, if there is a solid edge between the userand the content item in the first column of FIG. 6 but without an edgein the second column for the same user and content items, it revealsthat although the user did review the content item, the user did notclick-through the content item. In this case, in the fused graph, thereis a dotted edge between the user and the content item, as illustratedbetween content item 602 a and user 604 a. It is understood that theuser activities that may be captured in the user activity information600 is not limited to the activities as illustrated in FIG. 6 or engagedby the particular set of user as illustrated. It is also not limitedmerely to the dimension as illustrated. For example, a session basedapproach may be employed to capture information related to useractivities. In such a session-based approach, activities of all userswho are present in a session and whose activities exhibited in thesession (e.g., a period of time) may be considered or captured as whole.As an illustration, the total number of clicks on a content item by allusers in a session may be considered as the click counts of that contentitem in the session. Thus, if a number of users viewed a content itembut none of them clicked the content item, then there may be a dottededge between the session and the content item in the third column ofFIG. 6. By comparison, if a number of users viewed a content item andsome of them (even only one) clicked then content item, then there maybe a solid edge between the session and the content item in the thirdcolumn of FIG. 6.

It should be appreciated that the activity information 600 is notlimited to click activities and non-click activities as illustrated inthis example. User activity information 600 may include other types ofuser activities. For example, scrolling activities, typing activities,content interaction activities (e.g., whether the user play a gameembedded in content item, did the user achieve an objective of the game,etc.) and/or any other user activities during viewing of the contentitems may be included in the user activity information 600.

Returning to FIG. 5, the activity based content similarity determinationmodule 510 may be configured to determine similarities between contentitems based on the user activity information obtained and analyzed bythe user content activity information module 506. The operation(s) thatmay be implemented by the activity based content similaritydetermination module 510 is conceptually illustrated in FIG. 7.

FIG. 7 conceptually illustrates a similarity between two content itemsmay be determined based on user activity information. As shown in theexample, user activity information 702 a regarding content item #i, forexample as obtained and analyzed by the user content activityinformation module 506, indicates content item #i was viewed by a firstset of users, and whether those users have clicked content item #iduring viewing of the content item #i. As also shown, user activityinformation 702 b indicates content item #j was viewed by a second setof users, and whether those users have clicked content item #j duringviewing of the content item #j. As illustrated, common users in thefirst set and second set may be obtained. Those common users are theusers that have viewed both content item #i and #j. As illustration,suppose those common users may comprise users A, B, C, D, E, F as shown,and their click activities (or lack thereof) during the viewing of bothcontent item #i and #j may be determined. That is, as illustrated, usersA and B clicked content item #i when they were viewing it, while usersC, D, E, F did not click content item #i when they were viewing it.Similarly, as also shown, users B and D clicked content item #j whenthey were viewing content item #j, while users A, C, E, F did not clickcontent item #j when they were viewing content item #j. As stillillustrated, user activity vectors 704 a-b may be generated based on theactivity information 702 a and 702 b. As shown, the user activity vector704 a may be generated such that each component in the user activityvector 704 a correspond to a user A, B, C, D, E, and F, in that order,and the components of the vector 704 a indicate whether a correspondinguser has clicked content item #i. As shown, since users A and B clickedcontent item #i, and users C, D, E and F did not, the user activityvector 704 a is generated as (1, 1, 0, 0, 0, 0) with “1” indicating thecorresponding user (i.e., users A and B) clicked content item #i and “0”indicating the corresponding users (i.e., users C, D, E, and F) did notclick content item #i when they view the content item #i. Similarly, anactivity vector 704 b may be generated based on the user activityinformation 702 b as illustrated. As will be described below, vectors704 a-b may be used to determine a similarity between items #i and #j.

FIG. 8 illustrates another example of determining a similarity betweentwo content items #i and #j. As shown in this example, weight factor maybe assigned to specific user activities when generating the useractivity vector. Different user activities during viewing of the contentitems #i or #j by the users that viewed both content items #i and #j maybe differentiated when generating the user activity vectors. Forexample, user activities such as click activities, non-clickingactivities (i.e., viewed but not clicked content item) scrollactivities, dwell time activities (time duration spent on a content itemor a portion of the content item) may be accorded with differentweights. A number of weighting schemes based on types of user activitiesand as well as specifics of the user activities may be used. Forinstance, user click activities may be accorded a first weight W1, userscroll activities may be accorded with weight W2, and user non-clickingactivities may be accorded with a negative weight (user viewing of thecontent item but not clicking may indicate a negative feedbackreflecting the user is not interested in the content item). In anotherexample, edges shown in FIG. 6 may be weighted independently based onthe specifics of a user activity represented by the edge. For instance,as an illustration, a solid edge may be weighted by the dwell time ofcorresponding users—i.e., the length of viewing time by thecorresponding users on the entire content item or portion(s) of thecontent item. In another example, a solid edge may be weighted by thescroll activities and links or positions of clicking, during the viewingof the content item by the corresponding users.

As illustrated, user activity vectors 802 a-b may be generated based onthe weighted value corresponding to individual ones of the user thathave viewed both content items #i and #j. As also illustrated, the useractivity vectors 802 a-b may be used to determine a similarity betweencontent items #i and #j. Any known methods may be used to compute thesimilarity between content items #i and #j based on the user activityvectors 802 a-b. In this example, two of such methods are illustrated.One approach is to compute the similarity based on the cosine betweentwo vectors. Another exemplary approach is to compute a similaritybetween two vectors by employing a mutual information function. FIG. 9conceptually illustrates that a similarity between content items may bedetermined using user activity vectors associated with content items. Asillustrated, a point 902 may correspond to an M dimensional useractivity vector regarding content item #1 (e.g., the user activityvector 802 a shown in FIG. 8). Similarly, a point 904, as illustrated,may correspond to an another M dimensional user activity vectorregarding content item #2 (e.g., the user activity vector 802 b shown inFIG. 8). The similarity between content items #1 and #2 may bedetermined by computing a cosine of the angle between vector for contentitem #1 (corresponding to point 902) and vector for content item #2(corresponding to point 904) in the M dimensional space as shown. Inimplementations, the cosine-based similarity between content items #iand #j may be computed as follows:

${{{Sim}\left( {i,j} \right)} = {{{Cos}\; {{Sim}\left( {{vector}_{i},{vector}_{j}} \right)}} = {\sum\limits_{k = 1}^{N}\; {w_{i,k}*{w_{j\; k}/\sqrt{\sum\limits_{k = 1}^{N}\; {w_{i,k}}^{2}}}\sqrt{\sum\limits_{k = 1}^{N}\; {w_{j,k}}^{2}}}}}},,$

-   -   wherein vector_(i) and vector_(j) represent user activity        vectors associated with the content items #i and #j,        respectively, regarding a set of users that viewed both content        items #i and #j; and W_(i,k) represents a weighted value of a        component k in vector_(i), the component k corresponding to Kth        user in the set of users, and W_(j,k) represents a weighted        value of a component k in vector_(j), W_(i,k) and W_(j,k)        indicating the Kth user's activity during viewing content items        #i and #j, respectively.

As mentioned, the similarity between content items #i and #j may also becomputed based on their respective user activity information using amutual information function as follows:

-   -   Sim (i, j)=I (X_(i) and X_(j)), wherein X_(i) and X_(j) are        random variables based on W_(i,k) and W_(j,k),    -   K=1, N (the observations of X_(i) and X_(j)), and I denotes a        mutual information function that computes the mutual information        of variables X_(i) and X_(j)        Other exemplary methods for computing the activity similarity        between content items #i and #j may also be employed and are all        within the scope of the present teaching. For example, the        similarity between content items #i and #j may be computed using        a locality sensitive hashing algorithm (LSH) via a hamming        distance, a Euclidean distance, or any type of distance between        the user activity vectors associated with content items #i and        #j.

FIG. 10 illustrates a flowchart of an exemplary process 1000 fordetermining a similarity between two content items, according to oneembodiment of the present teaching. In some implementations, the process1000 may be implemented by an activity based content similaritydetermination module substantially similar to or the same as activitybased content similarity determination module 610 as described andillustrated herein. As illustrated, at 1002, a first set of users thathave viewed content item #1 may be determined. At 1004, a second set ofusers that have viewed content item #2 may be determined. At 1006, useractivity information regarding the users in the first set when viewingcontent item #1 may be obtained, and user activity information regardingthe user in the second set when viewing content item #2 may be obtained.At 1008, users that viewed both content item #1 and #2 are determined.At 1010, user activity vectors may be generated for content items #1 and#2, indicating user activities during the viewing of the content itemsmay be generated. At 1012, a similarity between content items #1 and #2may be generated based on the user activity vectors determined at 1008.

FIG. 11 illustrates the flowchart of another exemplary process 1100 fordetermining a similarity between two content items, according to oneembodiment of the present teaching. The difference between FIGS. 10 and11 is that at 1110 the user activities of the common users duringviewing of the content items #1 and #2 may be weighted as describedabove, and at 1112 the user activity vectors for content items #1 and #2may be generated based on the user activities weighted at 1110.

Returning to FIG. 5, the content item click probability determinationmodule 508 may be configured to estimate the likelihood that a userclicks a given content item. FIG. 12 illustrates a flowchart of anexemplary process for estimating the likelihood that a user will click agiven content item. The exemplary process shown in FIG. 12 may becarried out by a content item click probability determination modulesubstantially similar to or the same as the content item clickprobability determination module 508 as described and illustratedherein. As illustrated, at 1202, a content item #j and as well asinformation regarding a user may be received. At 1204, a set of contentitems related to content item #j may be determined based on the userinformation received at 1202. This set of related content items mayinclude content items that were viewed by the user during the samesession as content item #j, content items related to content item #jbased on relevance in accordance with a user profile of the user andwere viewed by the user in previous session(s), content items that werespecified (e.g., by a provider, administrator, moderator, and/or anyother entities related to the system 100) as being related to contentitem #j (e.g., based on a user type of the user and were viewed by theuser in previous session(s)), and/or any other content items beingconsidered related to content item #j in accordance with any othercriterion. At 1206, for each content item in the set determined at 1204,a similarity between the content item in the set and content item #j maybe determined. The similarities determined at 1206 may be expressed as(I_(s1), I_(s2), . . . I_(sN)), wherein I_(sN) denotes the similaritybetween Nth related content item in the set and content item #j. At1208, the likelihood that the user will click content item #j may becomputed based on the similarities determined at 1206. Any known methodsmay be employed to compute this likelihood. For example, the likelihoodthat the user will click content item #j may be computed using thefollowing formula:

${score} = \frac{\sum\limits_{i^{\prime} \in I}\; {s_{i^{\prime}}{act}_{i^{\prime}}}}{\sum\limits_{i^{\prime} \in I}\; s_{i^{\prime}}}$

wherein act_(i) denotes whether the ith related content item in the setwas clicked by the user (e.g., the value of act may be either 0 or 1),S_(i) denotes the similarity between content item #j and the ith relatedcontent item, and Score denotes the computed click probability ofcontent item #j by the user. Another example of computing the likelihoodof the user clicking content item #j may be computed as follows:

${score} = \frac{{\sum\limits_{i^{\prime} \in I}\; {s_{i^{\prime}}{act}_{i^{\prime}}}} + {\lambda \cdot {ctr}}}{{\sum\limits_{i^{\prime} \in I}\; s_{i^{\prime}}} + \lambda}$

wherein λ denotes a scale factor and ctr denotes the historical clickthrough rate of content item #j. Still in another example, the clickprobability may be computed as follows:

${score} = \frac{{\sum\limits_{i^{\prime} \in I}\; {s_{i^{\prime}}{{act}_{i^{\prime}}/{I}}}} + {\lambda \cdot {ctr}}}{{\sum\limits_{i^{\prime} \in I}\; {s_{i^{\prime}}/{I}}} + \lambda}$

wherein |I| denotes the cardinality.

Referring back to FIG. 1, the unified ranking unit 102 as illustratedmay be configured to receive a first set of candidate content items asgenerated by the user-profile based candidate content selection unit 106and a second set of candidate content items as generated by theuser-activity based candidate content selection unit 104. The unifiedranking unit 102 may be configured to rank the candidate content itemsfrom both sources. The ranking may be carried out in accordance with alearning model derived via the machine learning engine 108.

FIG. 13 is an exemplary diagram of the machine learning engine 108 shownin FIG. 1, according to one embodiment of the present teaching. Asillustrated herein, the machine learning engine 108 may be configured toinclude storage 1302, a model learning module 1308, and/or any othermodules. The storage 1302 may include data storage 1304 for temporallyor permanently storing training data received by the machine learningengine 108. In some implementations, the training data 1304 may bereceived by the machine learning engine 108 offline. In someembodiments, the training data 1304 may include features related tousers, features related to content items, user-content cross features,and/or any other features that may be deemed useful in machine learning.FIG. 14 provides some exemplary types of features related to users. FIG.15 provide a exemplary types of features related to content items.

The model learning module 1308 may be configured to receive the trainingdata, information deemed relevant to a learning model to be trained,and/or parameters to be employed in training the learning model based onthe training data. Such training data may reveal much informationrelated to user's interests in content items and is utilized by thepresent teaching to train a learning model to be used to rank candidatecontent items. Training data may include user information such asdemographics, declared interests, past interests in content items, etc.Training data may also include content information such asclassification of content items. Training data may also include eventinformation such as actions users performed on certain content items.

Features vectors may be generated based on received training data. Forexample, based on user information as illustrated in FIG. 14, userfeature vectors may be generated. Another type of feature vectors may begenerated based on information regarding content items, as illustratedin FIG. 15. User information and content information may be crossreferenced so that feature vectors may be constructed indicating theuser-content cross features. For example, with respect to a user and acontent item, whether the user is interested in one or more topicsincluded in the content item may be utilized to construct a user-contentcross feature vector, via user activities. If the user clicks a contentitem having a particular labeled topic, this activity may be convertedto a “1” attribute value in a user-content cross feature vector for thefeature corresponding to the topic. For each such a user activity, acorresponding user-content cross feature vector may be fed into themodel learning module 1308 to derive or update a model capturing thepreferences of users in content items.

During the training, the model learning module 1308 may use rulesconfigured in the system and stored in storage 1306. Such rules and/orthe parameters to be used in training a learning model may vary with thelearning model adopted. In some embodiments, to train a learning model,the model learning module 1308 may generate feature vectors first basedon the training data. As discussed above, user information may be usedby the model learning module 1308 to generate a multiple dimensionalfeature vector based on various characteristics of the user, e.g., asshown in FIG. 14. For instance, each dimension may be related to aparticular user characteristic such as user demographics or userinterests. For each dimension, the model learning module 1308 mayconstruct a feature vector. For example, given a user's age, the modellearning module 1308 may construct a user age vector based on a user'smanifested age by assigning the user to one of multiple (10) age groups,e.g., age groups corresponding to age ranges of 0-10, 10-20, 20-30,30-40, 40-50, 50-60, 60-70, 70-80, 80-90, 90-100. As an illustration,assume that an age feature vector is constructed based on the abovegroup of 10 age ranges. If a user's age is 15, the machine learningmodule 1308 may populate the age feature vector for the user as, e.g.,(0,1,0,0,0,0,0,0,0,0,0), indicative of the age group that the userbelongs to. Based on user information, multiple feature vectors may beconstructed. The user interests feature vector may be constructed withrespect to category and/or topics of interests based on, e.g., sometaxonomy. Such feature vectors map a user into a high dimensional spaceas a point. Users with similar features will be mapped to points in thehigh dimensional space with close distances and, hence, indicating thatthey share similar characteristics. Each such mapped point may beidentified based on the identification information of the underlyinguser.

Similarly, feature vectors associated with content items may also beconstructed for learning purposes. For example, a feature vector relatedto categories and/or topics of a content item may be constructed.Different topics/categories may be pre-classified into different codedgroups. Each content item may have identifiers that indicate thecategory/topic group(s) that the content item is classified into. Tocharacterize a content item in terms of content category/topic, afeature vector with all possible categories/topics may be constructedwith, e.g., each feature value being 1 (belonging to the category/topic)or 0 (not belonging to the category/topic). With respect to each contentitem, its corresponding feature vector is then constructed to have,e.g., “1”s on attributes representing categories/topics that the contentitem belongs to and “0”s on attributes representing categories/topicsthat the content item does not belong to. Feature vectors for othercharacteristics of a content item may also be constructed. That is, foreach content item, more than one feature vectors may be constructed andin combination, it maps a content item into a multiple dimensional spaceas a point. Each point mapped to the content space may be identifiedbased on the identification of the content items. Points mapped close toeach other in this content item space indicate that they share certaincommon or close characteristics.

Another exemplary type of feature vector that the model learning module1308 may utilize, as discussed above, is user-content cross featurevector. Based on received information on a user and content itemsassociated with the user, the model learning module 1308 may constructan integrated feature space that combine the user features with contentfeatures. For each user, it may be combined with different content itemsto generate different feature points in the user-content space. Auser-content cross feature vector may also be generated by the modellearning module 1308 based on each given situation involving a user anda content item. For example, a user-content cross feature vector may beconstructed based on different combinations of {user_id, user_feature}and {content_id, content_features} so that the user-content crossfeature vector may be constructed based on different ranges of thosefeature value combinations. In this case, depending on a specific userand a given content item, the model learning module 1308 may generate auser-content cross feature vector by assigning a “1” for theuser-content feature range appropriate for the given situation andassigning “0”s for all other ranges. For example, if a user is in theage group of 30-40 corresponding to #4 age group and the given contentitem has a content topic falling in the #13 content category/topic. Sucha user-content cross feature vector generated by the model learningmodule 1308 corresponds to a point in the user-content space. Pointshaving a close distance in this user-content space may signify thatthose users share some common interests in content of certain topics.

Once the training data points have been mapped to the feature space(s)(user space where users are mapped to via user related feature vectors,content space in which content items are mapped to via content relatedfeature vectors, and user-content space in which user-content crossfeature vectors are mapped), the model learning module 1308 carries outthe learning using such training data based on rules associated with thelearning model to be trained, as well the parameters in connection withthe learning model to be trained. As all the training data points havebeen mapped to the feature space, what may be learned include whichusers share similar interests and on what topics, content items, etc.What may also be learned may include what content items have caused moreactive involvement of the users who do or do not share similarinterests. Such knowledge is learned by the model learning module 1308and embedded in the learning model to be trained. Ultimately, the modellearning module 1308 may identify how the training data is clustered inthe high dimensional space and how the points clustered together may berelated to each other and in what manner as well as how data distributedin different clusters may be distinguished based on what features. Suchknowledge is captured in the learning process via the learning model andparameters associated therewith so that the learned models can beutilized, in the future, to determine what appropriate content items areto be selected for which user.

Below, a probabilistic model and learning scheme is disclosed herein. Itshould be appreciated, however, that any suitable model may be trainedby model learning module 1308. In addition, any learning model may beadopted to learn the relationship between user information and contentinformation via the linkage established via user activities. Oneexemplary model for learning is a probabilistic model, as illustratedbelow:

-   -   Pr(Click|F)=Φ(βF), where F is feature vectors (e.g., user        feature vector, content feature vector, user-content feature        vector) translated from user content viewing events and β is the        weighting parameter. Φ is the CDF of the standard normal        distribution.        As shown, the probabilistic model may be trained using feature        vectors generated based on, e.g., user information, content        information, as well as information about user-content        interactions. That is, every time a user content viewing event        occurs, information about the event may be converted into        feature vector(s) as described above, and used by the model        learning module 1308 to update a current probabilistic model,        e.g., with respect to the weights and/or parameters of the        underlying model employed. Such modified (updated) model may        then be stored and used in the future in subsequent content        rankings and/or selections. In this way, a model may be trained        in an expectation propagation manner using message passing        technique in a factor graph.

FIG. 16 illustrates an exemplary system diagram showing the unifiedranking unit 102 shown in FIG. 1. In this illustrated example, theunified ranking unit 102 may comprise multiple ranking units—i.e.,ranking units 1602 a, 1602 n, 1602 x, each of which may be configured toindependently to rank content items. For example, a ranking unit, e.g.,1602 a, may be configured to rank content items based on theirpopularity in a predetermined period. The unified ranking unit 102 mayalso include a ranking unit, such as ranking unit 1602 n, to rank thecandidate content items from the first and second candidate contentsets, generated based on user profile and user activities, respectively,based on trained learning models. For example, given a trainedprobabilistic model and the inputs related to a specific user andcandidate content items (in both first and second sets), the candidatecontent items can be ranked using the probabilistic model by computing,for each candidate content item, the probability that the given userwill click on the candidate content item. The higher the probability,the high the rank of the candidate content item.

As illustrated above, one example of the model trained by the machinelearning engine 108 may include a probabilistic model, which may be usedto rank a given candidate content item by estimating a probability ofclicking the given content item using Pr(Click|F)=Φ(βF), wherein Fdenotes features vectors (e.g., user feature vectors, content featurevectors, user-content cross feature vectors, and/or any other featurevectors) generated based on a given content item to be ranked for auser, and Pr(click|F) denotes a probability of the user clicking thegiven content item as identified by these feature vectors.

As still shown in FIG. 16, the rankings of candidate content itemsdetermined by various ranking units 1602 may be sent to the scoringmodule 1604 for generating an integrated ranking score based on therankings of the candidate content items from various ranking units 1602a, . . . , 1602 n, . . . 1602 x. In some embodiments, the scoring module1604 may employ certain approach to combine the rankings from differentranking units. For example, a weighted sum may be adopted, in which theweights may be applied to the ranking results from different rankingunits and such weights may be determined a priori (based onpre-determined preferences of each ranking unit) or learned via, e.g.,feedback information. The result from the scoring module 1604 mayprovide an order to the candidate content items sequenced based on theirranking scores. For example, the order can be an ascending order so thatthe candidate content items with the highest ranking appears first andthe candidate content items with lower rankings following that withhigher rankings Such an order may be used as the order in which thecandidate content items are presented to the user so that the contentitems that have a high likelihood to be clicked by the user arepresented first to improve the possibility that the recommended contentmeets the interests of the user, determined based on both the userprofile as well as the user activities.

FIG. 17 depicts the architecture of a computing device which can be usedto realize a specialized system implementing the present teaching. Sucha specialized system incorporating the present teaching has a functionalblock diagram illustration of a hardware platform which includes userinterface elements. The computer may be a general purpose computer or aspecial purpose computer. Both can be used to implement a specializedsystem for the present teaching. This computer 1700 may be used toimplement any component of the enhanced content recommendationtechniques, as described herein. For example, the personalized contentrecommendation module 100, etc., may be implemented on a computer suchas computer 1700, via its hardware, software program, firmware, or acombination thereof. Although only one such computer is shown, forconvenience, the computer functions relating to the personalized contentrecommendation module 100 may be implemented in a distributed fashion ona number of similar platforms, to distribute the processing load.

The computer 1700, for example, includes COM ports 1750 connected to andfrom a network connected thereto to facilitate data communications. Thecomputer 1700 also includes a central processing unit (CPU) 1720, in theform of one or more processors, for executing program instructions. Theexemplary computer platform includes an internal communication bus 1710,program storage and data storage of different forms, e.g., disk 1770,read only memory (ROM) 1730, or random access memory (RAM) 1740, forvarious data files to be processed and/or communicated by the computer,as well as possibly program instructions to be executed by the CPU. Thecomputer 1700 also includes an I/O component 1760, supportinginput/output flows between the computer and other components thereinsuch as user interface elements 1780. The computer 1700 may also receiveprogramming and data via network communications.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein (e.g., personalized content recommendation module 210, and/orother components of system 100 described herein). The hardware elements,operating systems and programming languages of such computers areconventional in nature, and it is presumed that those skilled in the artare adequately familiar therewith to adapt those technologies to enhancepersonalized content recommendation described herein. A computer withuser interface elements may be used to implement a personal computer(PC) or other input of work station or terminal device, although acomputer may also act as a server if appropriately programmed. It isbelieved that those skilled in the art are familiar with the structure,programming and general operation of such computer equipment and as aresult the drawings should be self-explanatory.

FIG. 18 depicts the architecture of a mobile device which can be used torealize a specialized system implementing the present teaching. In thisexample, the user device on which content and advertisement arepresented and interacted-with is a mobile device 1800, including, but isnot limited to, a smart phone, a tablet, a music player, a handledgaming console, a global positioning system (GPS) receiver, and awearable computing device (e.g., eyeglasses, wrist watch, etc.), or inany other form factor. The mobile device 1800 in this example includesone or more central processing units (CPUs) 1840, one or more graphicprocessing units (GPUs) 1830, a display 1818, a memory 1860, acommunication platform 1810, such as a wireless communication module,storage 1890, and one or more input/output (I/O) devices 1850. Any othersuitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 1800.As shown in FIG. 18, a mobile operating system 1870, e.g., iOS, Android,Windows Phone, etc., and one or more applications 1880 may be loadedinto the memory 1860 from the storage 1890 in order to be executed bythe CPU 1840. The applications 1880 may include a browser or any othersuitable mobile apps for receiving and rendering content streams andadvertisements on the mobile device 1800. User interactions with thecontent streams may be achieved via the I/O devices 1850 and provided topersonalized content recommendation module 100, and/or other componentsof system 10,

Hence, aspects of the methods of enhancing ad serving and/or otherprocesses, as outlined above, may be embodied in programming. Programaspects of the technology may be thought of as “products” or “articlesof manufacture” typically in the form of executable code and/orassociated data that is carried on or embodied in a input of machinereadable medium. Tangible non-transitory “storage” input media includeany or all of the memory or other storage for the computers, processorsor the like, or associated modules thereof, such as varioussemiconductor memories, tape drives, disk drives and the like, which mayprovide storage at any time for the software programming.

All or portions of the software may at times be communicated through anetwork such as the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma management server or host computer of personalized contentrecommendation module 100 into the hardware platform(s) of a computingenvironment or other system implementing a computing environment orsimilar functionalities in connection with personalized contentrecommendation module 100. Thus, another input of media that may bearthe software elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links or the like, also may be considered as mediabearing the software. As used herein, unless restricted to tangible“storage” media, terms such as computer or machine “readable medium”refer to any medium that participates in providing instructions to aprocessor for execution.

Hence, a machine-readable medium may take many forms, including but notlimited to, a tangible storage medium, a carrier wave medium or physicaltransmission medium. Non-volatile storage media include, for example,optical or magnetic disks, such as any of the storage devices in anycomputer(s) or the like, which may be used to implement the system orany of its components as shown in the drawings. Volatile storage mediainclude dynamic memory, such as a main memory of such a computerplatform. Tangible transmission media include coaxial cables; copperwire and fiber optics, including the wires that form a bus within acomputer system. Carrier-wave transmission media may take the form ofelectric or electromagnetic signals, or acoustic or light waves such asthose generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media thereforeinclude for example: a floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any otheroptical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM,any other memory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer may read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to a physicalprocessor for execution.

Those skilled in the art will recognize that the present teachings areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution—e.g., an installation on an existing server. In addition,the enhanced personalized content recommendation disclosed herein may beimplemented as a firmware, firmware/software combination,firmware/hardware combination, or a hardware/firmware/softwarecombination.

While the foregoing has described what are considered to constitute thepresent teachings and/or other examples, it is understood that variousmodifications may be made thereto and that the subject matter disclosedherein may be implemented in various forms and examples, and that theteachings may be applied in numerous applications, only some of whichhave been described herein. It is intended by the following claims toclaim any and all applications, modifications and variations that fallwithin the true scope of the present teachings.

We claim:
 1. A method, implemented on a machine having at least oneprocessor, storage, and a communication platform connected to a network,for recommending, to a user, content items, the method comprising:obtaining a user profile characterizing interests of the user;generating a first set of candidate content items based on the userprofile; generating a second set of candidate content items based on alikelihood that the user clicks a corresponding candidate content itemin the second set, wherein each likelihood is estimated based onsimilarities between the candidate content items in the second set andone or more content items that were previously viewed by the user;ranking each of the candidate content items in the first set and thesecond set; and providing, based on the rankings, the candidate contentitems in the first and second sets as content recommendations to theuser.
 2. The method of claim 1, wherein the likelihood of a candidateitem in the second set is estimated by: obtaining a set of relatedcontent items that are related to the candidate item and that werepreviously viewed by the user; determining, for each related contentitem, a similarity between the related content item and the candidatecontent item; determining the likelihood of the candidate content itembeing clicked by the user by aggregating the similarities between thecandidate content item and the related content items; and including thecandidate content item in the second set of candidate content items ifthe likelihood exceeds a certain threshold.
 3. The method of claim 2,wherein the step of determining the similarity comprises: determining aset of common users that viewed both the candidate content item and therelated content item; obtaining information related to one or moreactivities associated with the set of common users and engaged by thecommon users during viewing of the candidate content item and therelated content item; and computing the similarity based on theinformation related to one or more activities associated with the set ofcommon users.
 4. The method of claim 3, wherein the one or moreactivities includes at least one of clicking, typing, and/or scrolling.5. The method of claim 3, wherein the step of computing comprises:generating a first user activity vector for the candidate content itemand a second user activity vector for the related content item based onthe information; and estimating the similarity based on the first andsecond user activity vectors.
 6. The method of claim 4, wherein the stepof estimating is based on at least one of a cosine function, a mutualinformation algorithm, and a locality sensitive hashing (LSH) algorithm.7. The method of claim 1, wherein the step of ranking comprises rankingeach of the candidate content items in the first set and the second setusing a learning model, the learning model having been trained with atleast one of user feature information, content feature information anduser-content cross feature information.
 8. A system, implemented on amachine having at least one processor, storage, and a communicationplatform connected to a network, for recommending, to a user, contentitems, the system comprising: a user-profile based candidate contentselection unit configured to: obtain a user profile characterizinginterests of the user, and generate a first set of candidate contentitems based on the user profile; a user-activity based candidate contentselection unit configured to generate a second set of candidate contentitems based on a likelihood that the user clicks a correspondingcandidate content item in the second set, wherein each likelihood isestimated based on similarities between the candidate content items inthe second set and one or more content items that were previously viewedby the user; and a unified ranking unit configured to rank each of thecandidate content items in the first set and the second set, andprovide, based on the rankings, the candidate content items in the firstand second sets as content recommendations to the user.
 9. The system ofclaim 8, wherein the user-activity based candidate content selectionunit is configured such that the likelihood of a candidate item in thesecond set is estimated by: obtaining a set of related content itemsthat are related to the candidate item and that were previously viewedby the user; determining, for each related content item, a similaritybetween the related content item and the candidate content item;determining the likelihood of the candidate content item being clickedby the user by aggregating the similarities between the candidatecontent item and the related content items; and including the candidatecontent item in the second set of candidate content items if thelikelihood exceeds a certain threshold.
 10. The system of claim 8,wherein the user-activity based candidate content selection unit isconfigured such that determining the similarity comprises: determining aset of common users that viewed both the candidate content item and therelated content item; obtaining information related to one or moreactivities associated with the set of common users and engaged by thecommon users during viewing of the candidate content item and therelated content item; and computing the similarity based on theinformation related to one or more activities associated with the set ofcommon users.
 11. The system of claim 10, wherein the one or moreactivities includes at least one of clicking, typing, and/or scrolling.12. The system of claim 10, wherein the user-activity based candidatecontent selection unit is further configured such that computing thesimilarity based on the information related to one or more activitiesassociated with the set of common users comprises: generating a firstuser activity vector for the candidate content item and a second useractivity vector for the related content item based on the information;and estimating the similarity based on the first and second useractivity vectors.
 13. The system of claim 12, wherein the similarity isestimated based on at least one of a cosine function, a mutualinformation algorithm, and a locality sensitive hashing (LSH) algorithm.14. The system of claim 8, wherein the unified ranking unit isconfigured such that ranking each of the candidate content items in thefirst set and the second set comprises ranking each of the candidatecontent items in the first set and the second set using a learningmodel, the learning model having been trained with at least one of userfeature information, content feature information and user-content crossfeature information.
 15. A machine-readable tangible and non-transitorymedium having information for recommending, to a user, content items,wherein the information, when read by the machine, causes the machine toperform the following: obtaining a user profile characterizing interestsof the user; generating a first set of candidate content items based onthe user profile; generating a second set of candidate content itemsbased on a likelihood that the user clicks a corresponding candidatecontent item in the second set, wherein each likelihood is estimatedbased on similarities between the candidate content items in the secondset and one or more content items that were previously viewed by theuser; ranking each of the candidate content items in the first set andthe second set; and providing, based on the rankings, the candidatecontent items in the first and second sets as content recommendations tothe user.
 16. The machine-readable and non-transitory medium of claim15, wherein the likelihood of a candidate item in the second set isestimated by: obtaining a set of related content items that are relatedto the candidate item and that were previously viewed by the user;determining, for each related content item, a similarity between therelated content item and the candidate content item; determining thelikelihood of the candidate content item being clicked by the user byaggregating the similarities between the candidate content item and therelated content items; and including the candidate content item in thesecond set of candidate content items if the likelihood exceeds acertain threshold.
 17. The machine-readable and non-transitory medium ofclaim 15, wherein the step of determining the similarity comprises:determining a set of common users that viewed both the candidate contentitem and the related content item; obtaining information related to oneor more activities associated with the set of common users and engagedby the common users during viewing of the candidate content item and therelated content item; and computing the similarity based on theinformation related to one or more activities associated with the set ofcommon users.
 18. The machine-readable and non-transitory medium ofclaim 17, wherein the one or more activities includes at least one ofclicking, typing, and/or scrolling.
 19. The machine-readable andnon-transitory medium of claim 17, wherein the step of computingcomprises: generating a first user activity vector for the candidatecontent item and a second user activity vector for the related contentitem based on the information; and estimating the similarity based onthe first and second user activity vectors.
 20. The machine-readable andnon-transitory medium of claim 15, wherein the step of ranking comprisesranking each of the candidate content items in the first set and thesecond set using a learning model, the learning model having beentrained with at least one of user feature information, content featureinformation and user-content cross feature information.